Generative AI Vs Agentic AI Vs AI Agents

Summary

In this video, Krishna explains the key differences between generative AI, AI agents, and agentic AI. He starts by discussing large language models (LLMs) and how they generate content like text, images, and audio based on input prompts. He then delves into AI agents, which are designed to handle specific tasks, using external data when necessary. Finally, he introduces agentic AI, where multiple AI agents collaborate to solve complex workflows, demonstrating the synergy between them. The video offers a clear understanding of how these concepts differ and their practical applications in AI development.

Takeaways

• 😀 Generative AI refers to large models like LLMs and image models that generate new content based on input prompts.
• 😀 These models are trained with vast amounts of data and can produce text, images, videos, and even audio.
• 😀 Tools like LangChain, Langraph, and OpenAI APIs are commonly used to develop generative AI applications, such as chatbots.
• 😀 A key feature of generative AI applications is that they are reactive, responding to prompts to generate content.
• 😀 AI agents are designed to handle specific tasks, often relying on external data sources or APIs to gather information.
• 😀 Unlike generative AI, AI agents perform defined tasks, such as searching the web for real-time information through tools like Tably.
• 😀 Tool calls are a crucial part of AI agents, allowing them to access external data when they cannot generate the required response on their own.
• 😀 Agentic AI takes the concept of AI agents further by enabling multiple agents to collaborate and communicate to complete a complex task.
• 😀 In agentic AI, each agent performs a specific sub-task (e.g., generating a transcript, creating a title, writing a description) as part of a larger workflow.
• 😀 AI agents in agentic AI systems can interact and exchange information with each other, making the entire system more efficient in achieving a common goal.
• 😀 The main difference between AI agents and agentic AI is that agentic AI involves collaboration among agents to solve complex workflows, while AI agents focus on individual tasks.

Q & A

What is the primary focus of this video?

The video focuses on explaining the differences between generative AI, AI agents, and agentic AI, and clarifying how each of these concepts works and their applications.

What are large language models (LLMs) and how do they work?

LLMs, like GPT-4 or Llama, are large models trained on massive datasets. They are designed to generate new content, such as text, images, or even video, based on the prompts provided to them. These models are reactive, meaning they generate content in response to specific input.

What is the significance of prompts in generative AI applications?

Prompts in generative AI serve as instructions given to the model, guiding its behavior to generate specific content. For instance, a prompt might ask an AI to act as a data scientist and conduct an interview. The model uses these prompts to create the required content.

What are some common libraries used in generative AI applications?

Popular libraries used in generative AI applications include LangChain, Langraph, LlamaIndex, and Grock. These libraries help in building applications that utilize LLMs and other tools to generate content.

How do AI agents work, and what is their role in task automation?

AI agents are specialized systems that perform specific tasks. For example, an AI agent might retrieve current news by calling an external API. AI agents are typically responsible for executing a single task, and they rely on external data sources or APIs when they lack necessary information.

What are tool calls in AI agents, and how do they function?

Tool calls are used by AI agents to access external data or services. For example, if an LLM doesn't have information on current news, it may make a tool call to an external service (like Tably) to retrieve the latest data and then process the response.

What distinguishes agentic AI from regular AI agents?

Agentic AI involves multiple AI agents working together to complete a complex task or workflow. While AI agents handle individual tasks, agentic AI systems require collaboration between agents to achieve a common goal, such as generating a blog post from a YouTube video.

Can you provide an example of an agentic AI application?

An example of agentic AI is the system where multiple agents collaborate to convert a YouTube video into a blog. The process involves tasks like generating a transcript, creating a title, writing a description, and drafting a conclusion, all handled by separate AI agents working together.

How do AI agents in agentic AI systems communicate with each other?

In agentic AI systems, agents communicate with each other by sharing outputs and inputs. For example, one agent may provide a title, which another agent uses to create a description. This collaboration allows for the completion of complex workflows in an organized manner.

What is the key difference between AI agents and agentic AI?

The key difference is that AI agents handle individual tasks, while agentic AI involves multiple agents collaborating to solve a larger, more complex goal. In agentic AI, agents are part of a system where they share information and work together to complete the task.